Category: Artificial Intelligence

For organizations with multiple AWS accounts, staying on top of planned AWS service changes and events is critical to keep operations and business running smoothly. Organizations use AWS Health for ongoing visibility into resource performance and the availability of AWS services and accounts, but the volume of notifications from AWS Health can sometimes be overwhelming. […]

Introduction In order to enforce best practices for governance and compliance across AWS accounts in a centralized way, AWS Control Tower is an easy place to start.  However, ensuring continuous compliance requires regular drift detection and remediation, which Control Tower facilitates by providing a mechanism to detect drift and publish notifications to Amazon Simple Notification […]

Keeping your AWS infrastructure up-to-date and secure is a critical part of maintaining a robust and reliable cloud environment. AWS Systems Manager’s patching capabilities are a powerful tool in this effort, allowing you to automatically apply the latest security updates and bug fixes to your managed nodes, including Amazon Elastic Compute Cloud (EC2) instances, on-premises […]

Generative AI can streamline the Correction of Errors process, saving time and resources. By using generative AI to leverage large language models, combined with the Correction of Errors process, businesses can expedite the identification and documentation of the cause of errors, while saving time and resources. Purpose and set-up The purpose of this blog is […]

AWS Control Tower is the easiest way to set up and govern a security, multi-account AWS environment. A key feature of AWS Control Tower is to deploy and manage controls at scale across an entire AWS Organizations. These controls are categorized based on their behavior and guidance. The behavior of each control is one of […]

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible […]

As applications scale, customers need more automated practices to maintain application availability and reduce the time and effort spent detecting, debugging, and resolving operational issues. Organizations allocate money and developer time to deploy and manage various monitoring tools, while also dedicating considerable effort to training teams on their usage. When issues arise, operators navigate through […]

Have you ever been investigating a problem and opened up a log file and thought “I have no idea what I am looking at. If only I could get a summary of the data.” Observability and log data play an important role in maintaining operational excellence and ensuring the reliability of your applications and services. […]

With the pace of innovation with Generative AI applications, there is increasing demand for more granular observability into applications using Large Language Models (LLMs). Specifically, customers want visibility into: Prompt metrics like token usage, costs, and model IDs for individual transactions and operations, apart from service-level aggregations. Output quality factors including potential toxicity, harm, truncation […]

With the emergence of generative AI being incorporated into every aspect of how we utilize technology, a common question that customers are asking is how to properly audit generative AI services on AWS, such as Amazon Bedrock, Amazon Sagemaker, Amazon Q Developer, and Amazon Q Business. In this post, we will demonstrate common scenarios that […]